October 2025 arXiv papers — page 112

Sherine George

Retrieval-Augmented Generation (RAG) couples a retriever with a large language model (LLM) to ground generated responses in external evidence. While this framework enhances factuality and domain adaptability, it faces a key bottleneck: balancing retrieval recall with limited LLM context. Retrieving too few passages risks missing critical context, while retri

Helia Hashemi, Victor Rühle, Saravan Rajmohan

Reasoning models have gained significant attention due to their strong performance, particularly when enhanced with retrieval augmentation. However, these models often incur high computational costs, as both retrieval and reasoning tokens contribute substantially to the overall resource usage. In this work, we make the following contributions: (1) we propose

Ben M. Andrew

Logical specifications are widely used to represent software systems and their desired properties. Under system degradation or environmental changes, commonly seen in complex real-world robotic systems, these properties may no longer hold and so traditional verification methods will simply fail to construct a proof. However, weaker versions of these properti

Karol Sajnok, Michał Matuszewski

Backpropagation learning algorithm, the workhorse of modern artificial intelligence, is notoriously difficult to implement in physical neural networks. Equilibrium Propagation (EP) is an alternative with comparable efficiency and strong potential for in-situ training. We extend EP learning to both discrete and continuous complex-valued wave systems. In contr

Keertana Chidambaram, Karthik Vinary Seetharaman, Vasilis Syrgkanis

Reinforcement Learning from Human Feedback (RLHF) has become central to aligning large language models with human values, typically by first learning a reward model from preference data which is then used to update the model with reinforcement learning. Recent alternatives such as Direct Preference Optimization (DPO) simplify this pipeline by directly optimi

Jaehan Kim, Minkyoo Song, Minjae Seo, Youngjin Jin

Credential stuffing attacks have caused significant harm to online users who frequently reuse passwords across multiple websites. While prior research has attempted to detect users with reused passwords or identify malicious login attempts, existing methods often compromise usability by restricting password creation or website access, and their reliance on c

Ivan Novikau, Ilon Joseph

The Carleman embedding method is a widely used technique for linearizing a system of nonlinear differential equations, but fails to converge in regions where there are multiple fixed points. We propose and test three different versions of a global piecewise Carleman embedding technique, based on partitioning space into multiple regions where the center and s

Simon M. Fröhlich, Tobias Sparmann, Maarten A. Brems, Jan Rothörl

Non-flat energy landscapes leading to localized pinning of skyrmions pose an inherent and unavoidable challenge for studies of fundamental 2D spin structure dynamics as well as applications. Accounting for pinning is a key requirement for predictive modeling of skyrmion systems, as it impacts the systems' dynamics and introduces randomizing effects. In this

Elias Hossain, Mehrdad Shoeibi, Ivan Garibay, Niloofar Yousefi

Interpreting gene clusters from RNA sequencing (RNA-seq) remains challenging, especially in antimicrobial resistance studies where mechanistic insight is important for hypothesis generation. Existing pathway enrichment methods can summarize co-expressed modules, but they often provide limited cluster-specific explanations and weak connections to supporting l

Lorenzo Caprini, Alessandro Petrini, Umberto Marini Bettolo Marconi

In this paper, we discuss microscopic models for chiral active particles, i.e., rotating active units that exhibit circular or spinning motion. While non-chiral active particles are typically governed by self-propulsion and conservative interactions, the rotating motion of chiral particles generates additional non-conservative forces that cannot be derived f

Shunzhi Pang

With the rise of emerging risks, model uncertainty poses a fundamental challenge in the insurance industry, making robust pricing a first-order question. This paper investigates how insurers' robustness preferences shape competitive equilibrium in a dynamic insurance market. Insurers optimize their underwriting and liquidity management strategies to maximize

Thomas Bernard, François Grondin, Jean-Michel Lavoie

This paper presents a practical alternative to programmable-logic-controller-centric automation by implementing an event-driven architecture built with industrial Internet of Things tools. A layered design on a local edge server (i) abstracts actuators, (ii) enforces mutual exclusion of shared physical resources through an interlock with priority queueing, (

Martín de Frutos, Laura Botero-Bolívar, Esteban Ferrer

This paper proposes a pitch control strategy to mitigate the underwater acoustic footprint of offshore wind turbines, a measure that will soon become necessary to minimize impacts on marine life, which rely on sound for communication, navigation, and survival. First, we quantify the underwater acoustic signature of blade-generated aerodynamic noise from thre

Italo Luis da Silva, Hanqi Yan, Lin Gui, Yulan He

Large Language Models (LLMs) show strong reasoning and text generation capabilities, prompting their use in scientific literature analysis, including novelty assessment. While evaluating novelty of scientific papers is crucial for peer review, it requires extensive knowledge of related work, something not all reviewers have. While recent work on LLM-assisted

Victor F. C. Vieira, Rafael P. Bernar, Caio F. B. Macedo

Tidal forces acting on orbiting bodies arise from inhomogeneities in the gravitational field, generating stresses that can deform or even disrupt these objects. In this work, we analyze relativistic tidal forces associated with ultracompact objects described by static and spherically symmetric spacetimes, focusing on observers in circular geodesic motion. We

Junjie Pan

Let $g$ denote a fixed holomorphic Hecke cusp form of weight $k \equiv 0 \pmod{4}$ on $\mathrm{SL}_2(\mathbb{Z})$, and let $\pi$ be a fixed cuspidal automorphic representation of $\mathrm{GL}_3$. In this paper, we establish an asymptotic formula for the first moment of the product \[L(1/2,g\times F)L(1/2,\pi\times F),\] where $F$ runs over an orthonormal bas

Mike Pols, Carl P. Romao, Dominik M. Juraschek

Ferrons are a type of quasiparticle corresponding to elementary excitations of the ferroelectric order. Analogously to how magnons modulate and transport magnetization, ferrons modulate and transport electric polarization. Here, we introduce multiferrons as elementary excitations with both electric and magnetic character. Multiferrons lead to a tilt and elli

Alex Gu, Bartosz Piotrowski, Fabian Gloeckle, Kaiyu Yang

Neural theorem proving has advanced rapidly in the past year, reaching IMO gold-medalist capabilities and producing formal proofs that span thousands of lines. Although such proofs are mechanically verified by formal systems like Lean, their excessive length renders them difficult for humans to comprehend and limits their usefulness for mathematical insight.

Virendra Nishad, Bhaskar Mukhoty, Hilal AlQuabeh, Sandeep K. Shukla

Deep neural networks have achieved remarkable success in a wide range of classification tasks. However, they remain highly susceptible to adversarial examples - inputs that are subtly perturbed to induce misclassification while appearing unchanged to humans. Among various attack strategies, Universal Adversarial Perturbations (UAPs) have emerged as a powerfu

Sebastian Brandt, Tim Göttlicher

In this work, we study the Lov\'asz local lemma (LLL) problem in the area of distributed quantum computing, which has been the focus of attention of recent advances in quantum computing [STOC'24, STOC'25, STOC'25]. We prove a lower bound of $2^{\Omega(\log^* n)}$ for the complexity of the distributed LLL in the quantum-LOCAL model. More specifically, we obta

Gopal Narmada Naidu, Omer Can Karaman, Giulia Tagliabue

Precise control of temperature fields at the micro- and nanoscale is essential for emerging applications in nanophotonics, catalysis, and microfluidics, yet remains difficult due to the diffusive nature of heat. While inverse-design algorithms have advanced thermoplasmonic metasurfaces, their extension to all-dielectric systems has not been explored. Here, a

Carlos Gamboa, Alexandre Street, Davi Valladão, Bernardo Pagnocelli

Traditional end-to-end contextual robust optimization models are trained for specific contextual data, requiring complete retraining whenever new contextual information arrives. This limitation hampers their use in online decision-making problems such as energy scheduling, where multiperiod optimization must be solved every few minutes. In this paper, we pro

Sheng Wang, Muhammad Maladoh Bah

European countries are ambitious in both the net-zero transition and offshore energy resource development. The Irish and UK governments announced their commitments to offshore wind capacities - 37 and 125 GW, respectively, in 2050, more than two times higher than their projected power demands. While other continental countries, such as Germany, are calling f

Marco Canteri, James Bate, Ida Mishra, Nicolai Friis

The ability to establish entanglement between the nodes of future quantum networks is essential for enabling a wide range of new applications in science and technology. A promising approach involves the use of a powerful central node capable of deterministically preparing arbitrary multipartite entangled states of its matter-based qubits and efficiently dist

Shengmao Zhu

Motivated by an amazing integrality structure conjecture for the $U(N)$ Chern-Simons quantum invariants of framed knots investigated by Mari\~no and Vafa, a new conjectural formula, named Hecke lifting conjecture, was proposed in \cite{CLPZ23} for framed links. This note is devoted to the study of this Hecke lifting conjecture. We prove this conjecture for t

Tian Guo, Emmanuel Hauptmann

In quantitative investing, return prediction supports various tasks, including stock selection, portfolio optimization, and risk management. Quantitative factors, such as valuation, quality, and growth, capture various characteristics of stocks. Unstructured data, like news and transcripts, has attracted growing attention, driven by recent advances in large

Shiwen Ou, Yuwei Li, Lu Yu, Chengkun Wei

Deep learning (DL) frameworks serve as the backbone for a wide range of artificial intelligence applications. However, bugs within DL frameworks can cascade into critical issues in higher-level applications, jeopardizing reliability and security. While numerous techniques have been proposed to detect bugs in DL frameworks, research exploring common API patte

Gebreslassie atsbha weldegebrial, hunduma legesse geleta

Many authors have examined various boundary behaviors of injective harmonic mappings in the open unit disk. Building on Laugesen's work, Bshouty and others explored the boundary behavior of harmonic mappings under different conditions. In this paper, we extend their work and find out the angular limits of the arguments and logarithms of analytic functions un

Joshua Wolfe Brook, Ilia Markov

This research introduces a novel approach to textual and multimodal Hate Speech Detection (HSD), using Large Language Models (LLMs) as dynamic knowledge bases to generate background context and incorporate it into the input of HSD classifiers. Two context generation strategies are examined: one focused on named entities and the other on full-text prompting.

Gerard Comas-Quiles, Carles Garcia-Cabrera, Julia Dietlmeier, Noel E. O'Connor

Unsupervised anomaly detection (UAD) presents a complementary alternative to supervised learning for brain tumor segmentation in magnetic resonance imaging (MRI), particularly when annotated datasets are limited, costly, or inconsistent. In this work, we propose a novel Multimodal Vision Transformer Autoencoder (MViT-AE) trained exclusively on healthy brain

Effrosyni Sokli, Pranav Kasela, Georgios Peikos, Gabriella Pasi

Dense Retrieval Models (DRMs) are a prominent development in Information Retrieval (IR). A key challenge with these neural Transformer-based models is that they often struggle to generalize beyond the specific tasks and domains they were trained on. To address this challenge, prior research in IR incorporated the Mixture-of-Experts (MoE) framework within eac

Jiaye Yang, Xinyu Zhao, Tianlong Chen, Kandyce Brennan

While Artificial Intelligence (AI) shows promise in healthcare applications, existing conversational systems often falter in complex and sensitive medical domains such as Sexual and Reproductive Health (SRH). These systems frequently struggle with hallucination and lack the specialized knowledge required, particularly for sensitive SRH topics. Furthermore, c

Ines Besrour, Jingbo He, Tobias Schreieder, Michael Färber

We present SQuAI (https://squai.scads.ai/), a scalable and trustworthy multi-agent retrieval-augmented generation (RAG) framework for scientific question answering (QA) with large language models (LLMs). SQuAI addresses key limitations of existing RAG systems in the scholarly domain, where complex, open-domain questions demand accurate answers, explicit clai

Prithwish Jana, Kaan Kale, Ahmet Ege Tanriverdi, Cruise Song

Translating human-written mathematical theorems and proofs from natural language (NL) into formal languages (FLs) like Lean 4 has long been a significant challenge for AI. Most state-of-the-art methods either focus on theorem-only NL-to-FL auto-formalization or on FL proof synthesis from FL theorems. In practice, auto-formalization of both theorem and proof

François Joint, Igor Lapkin, Pierre-Baptiste Vigneron, Emilie Hérault

We present the design, fabrication, and characterisation of a broadband vacuum window and infrared filter based on ultra-high molecular weight polyethylene (UHMWPE) for millimeter wave receivers operating across ALMA Band 6 and 7 (211-373 GHz). The window incorporates pyramidal anti-reflection (AR) structures, machined directly into the polyethylene using CN

Yuhong Cao, Yizhuo Wang, Jingsong Liang, Shuhao Liao

This work pushes the boundaries of learning-based methods in autonomous robot exploration in terms of environmental scale and exploration efficiency. We present HEADER, an attention-based reinforcement learning approach with hierarchical graphs for efficient exploration in large-scale environments. HEADER follows existing conventional methods to construct hi

Arpan Choudhury, Sonaldeep Halder, Rahul Maitra, Debashree Ghosh

An accurate description of strong correlation is quintessential for the exploration of emerging chemical phenomena. While near-term variational quantum algorithms provide a theoretically scalable framework for quantum chemical problems, the accurate simulation of multireference effects remains elusive, hindering progress toward the rational design of novel c

Natalie Behague

A finite set $X$ in a Euclidean space $\mathbb{R}^d$ is called Ramsey if for every $k$ there exists an integer $n$ such that whenever $\mathbb{R}^n$ is coloured with $k$ colours, there is a monochromatic copy of $X$. Graham conjectured that all spherical sets are Ramsey, but progress on this conjecture has been slow. A key result of K\v{r}\'{i}\v{z} is that

Lysander Huberich, Eve Ammerman, Gu Yu, Yining Ren

Understanding how atomic defects shape the nanoscale optical properties of two-dimensional (2D) semiconductors is essential for advancing quantum technologies and optoelectronics. Using scanning tunneling spectroscopy (STS) and luminescence (STML), we correlate the atomic structure and optical fingerprints of individual defects in monolayer MoS$_2$. A bilaye

Molly A. Thomas, Daniel Llewellyn, Patrick W. Yard, Benjamin A. Slater

The reliable distribution of high-dimensional entangled quantum states, an important resource in quantum technologies, through optical fibre networks is challenging due to the need to maintain coherence across multiple modes. Here we demonstrate the distribution of four-dimensional path-encoded entangled quantum states between photonic chips, enabled by a no

Yung-Chen Tang, Pin-Yu Chen, Andrea Cavallaro

Allocating more computation during inference time (test-time scaling) improves language model performance, especially for reasoning tasks. However, popular methods like Best-of-$N$ sampling often show diminishing returns as $N$ increases. To address this inefficiency, we introduce a general test-time calibration framework that adaptively modifies the model t

Antonyo Musabini, Rachid Benmokhtar, Jagdish Bhanushali, Victor Galizzi

This paper presents a novel dataset aimed at detecting pedestrians' intentions as they approach an ego-vehicle. The dataset comprises synchronized multi-modal data, including fisheye camera feeds, lidar laser scans, ultrasonic sensor readings, and motion capture-based 3D body poses, collected across diverse real-world scenarios. Key contributions include det

Lino Gerlach, Elliott Kauffman, Abhishikth Mallampalli

The CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm) project aims to detect anomalous physics signatures without bias from theoretical models in proton-proton collisions at the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider. CICADA identifies anomalies in low-level calorimeter trigger data using a convolutional autoen

Qiwei Wan, Yi Zhang

Regardless of model and platform details, the critical phenomena exhibit universal behaviors that are remarkably consistent across various experiments and theories, resulting in a significant scientific success of condensed matter physics. One widely known and commonly used example is the 2D quantum Hall transition; yet, its universal exponents still somewha

Paolo Giudici, Rosa C. Rosciano, Johanna Schrader, Delf-Magnus Kummerfeld

This paper introduces a novel methodology for constructing multiclass ROC curves using the multidimensional Gini index. The proposed methodology leverages the established relationship between the Gini coefficient and the ROC Curve and extends it to multiclass settings through the multidimensional Gini index. The framework is validated by means of two compreh

Veranika Boukun, Jörg Lücke

Variational autoencoders (VAEs) are among leading approaches to address the problem of learning disentangled representations. Typically a single VAE is used and disentangled representations are sought within its single continuous latent space. In this paper, we propose and provide a proof of concept for a novel Multi-Stream Variational Autoencoder (MS-VAE) t

Yameng Zhang, Dianye Huang, Max Q. -H. Meng, Nassir Navab

Freehand 3D ultrasound (US) imaging using conventional 2D probes offers flexibility and accessibility for diverse clinical applications but faces challenges in accurate probe pose estimation. Traditional methods depend on costly tracking systems, while neural network-based methods struggle with image noise and error accumulation, compromising reconstruction

Davi Oliveira Chaves, Chang Chiann, Pedro Alberto Morettin

In many scientific fields, such as agriculture, temperature time series are of interest both as explanatory variables and as objects of study in their own right. However, at the state level, incorporating information from all possible locations in an analysis can be overwhelming, while using a summary measure, such as the state-wide average temperature, can

Lei Shi, Gang Li, Junxing Zhang

Automatic medical image segmentation is a fundamental step in computer-aided diagnosis, yet fully supervised approaches demand extensive pixel-level annotations that are costly and time-consuming. To alleviate this burden, we propose a weakly supervised segmentation framework that leverages only four extreme points as annotation. Specifically, bounding boxes

Carlo Buccisano

The so called theory of derived D-modules is an extension of classical D-modules to derived algebraic geometry, which uses the derived information of the base scheme. We prove that the three different definitions of derived D-modules, given by Beraldo, Nuiten and To\"en-Vezzosi, on a (nice) derived scheme yield equivalent symmetric monoidal $\infty$-categori

Lucas Amoudruz, Sergey Litvinov, Costas Papadimitriou, Petros Koumoutsakos

Inverse problems are crucial for many applications in science, engineering and medicine that involve data assimilation, design, and imaging. Their solution infers the parameters or latent states of a complex system from noisy data and partially observable processes. When measurements are an incomplete or indirect view of the system, additional knowledge is r

Richard Sharp

We obtain a weighted equidistribution theorem for amenable skew product extensions of countable state Markov shifts satisfying the BIP property. We also show, without requiring the BIP property, that Gurevic pressure for an amenable skew product agrees with the Gurevic pressure for the abelianized system. This had been proved by Dougall and Sharp in the case

Sung-Soo Kim, Xiaobin Li, Futoshi Yagi, Rui-Dong Zhu

We propose a novel topological vertex formalism for 5d $\mathcal{N}=1$ SU($N$) gauge theory with a hypermultiplet in the symmetric tensor representation, whose Type IIB brane construction involves an NS5-brane attached to an O7$^+$-plane. Inspired by the identification $\mathrm{O7}^+\sim \mathbb{Z}_2 + 4 \mathrm{D7}$, we introduce two new types of vertices:

Mattéo Sautron, Jérôme Pétri, Dipanjan Mitra, Adélie Dupuy--Junet

Millisecond pulsars (MSPs) are the oldest but fastest pulsars known to date. In the 1980s, to explain how these pulsars could be formed, a new hypothesis was formulated: the recycling of pulsars, i.e the fact that a pulsar could accrete matter from a companion and been spun up. In this paper, we developed a population synthesis algorithm for pulsars which be

Jiaxin Li, Thanh Vu, Guangjun Zhu

We compute the depth of powers of edge ideals of integrally closed edge-weighted paths.

Rongfeng Su, Mengjie Du, Xiaokang Liu, Lan Wang

Phase-based features related to vocal source characteristics can be incorporated into magnitude-based speaker recognition systems to improve the system performance. However, traditional feature-level fusion methods typically ignore the unique contributions of speaker semantics in the magnitude and phase domains. To address this issue, this paper proposed a f

Wilfried Segnou, Riccardo Muolo, Marie Dorchain, Hiroya Nakao

The dynamics of coupled Stuart-Landau oscillators play a central role in the study of synchronization phenomena. Previous works have focused on linearly coupled oscillators in different configurations, such as all-to-all or generic complex networks, allowing for both reciprocal or non-reciprocal links. The emergence of synchronization can be deduced by provi

Juliette Robuschi, Ana López-Sepulcre, Cecilia Ceccarelli, Layal Chahine

Two main formation routes have been proposed for interstellar complex organic molecules (iCOMs): on dust grain surfaces and in the gas phase. Observing such molecules in protostellar outflow shock regions - provided that their ages are well-constrained - can help distinguish between these pathways by probing chemical evolution over time. This study focuses o

Frank Schweitzer, Georges Andres, Adrien Baut, Giona Casiraghi

We study the collective behavior in a stochastic agent-based model of active matter. Provided a critical take-up of energy, agents produce two types of goods $x$, $y$ that follow a generalized Lotka-Volterra dynamics. For isolated agents, production would either reach a fixed point or diverge. Coupling agents' production via a mean field of $x$, however, can

Lino Gerlach, Liv Våge, Thore Gerlach, Elliott Kauffman

Fast and efficient machine learning is of growing interest to the scientific community and has spurred significant research into novel model architectures and hardware-aware design. Recent hard? and software co-design approaches have demonstrated impressive results with entirely multiplication-free models. Differentiable Logic Gate Networks (DLGNs), for inst

Yuxi Lu, Catherine Manea, Maryum Sayeed, Stephanie T. Douglas

The question of whether genuinely young high-$\alpha$ stars exist has been discussed for over a decade since their discovery from asteroseismology of giant stars as it is challenging to break the degeneracy between the binary interaction and the genuinely young scenarios. Young high-$\alpha$ stars are hard to explain with traditional chemical evolution model

Yefan Zeng, Shengyu Duan, Rishad Shafik, Alex Yakovlev

The Tsetlin Machine (TM) offers high-speed inference on resource-constrained devices such as CPUs. Its logic-driven operations naturally lend themselves to parallel execution on modern CPU architectures. Motivated by this, we propose an efficient software implementation of the TM by leveraging instruction-level bitwise operations for compact model representa

Ahmad Raeisi, Mahdi Dolati, Sina Darabi, Sadegh Talebi

The growing demand for computational resources in machine learning has made efficient resource allocation a critical challenge, especially in heterogeneous hardware clusters where devices vary in capability, age, and energy efficiency. Upgrading to the latest hardware is often infeasible, making sustainable use of existing, mixed-generation resources essenti

Ziqian Li, Kang Liu, Yongcun Song, Hangrui Yue

Operator learning has emerged as a promising paradigm for developing efficient surrogate models to solve partial differential equations (PDEs). However, existing approaches often overlook the domain knowledge inherent in the underlying PDEs and hence suffer from challenges in capturing temporal dynamics and generalization issues beyond training time frames.

Stephen Michael Wright

Simultaneous variable selection and robust data fitting are important aspects of many mathematical modelling projects and a wide array of optimisation tools and techniques exist to support them. When the intention is to embed this capability in run-time interactive decision support tools running hundreds of such modelling tasks simultaneously on a GPU, the c

Dominik Burr, Rudi Reichenbächer, Christina Scheffler, Konrad Steiner

Continuous-fiber-reinforced polymers are vital for lightweight structural applications, where fiber-matrix wetting critically influences composite performance. Understanding dynamic wetting behavior during resin infiltration remains challenging, especially under realistic processing conditions. Here we investigate the dynamic wetting of commercial carbon fib

Daniel Vollmers, Hamada M. Zahera, Diego Moussallem, Axel-Cyrille Ngonga Ngomo

Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for entity recognition and disambiguation, which can be computationally intensive and less effective. We propose a fine-tuned model that jointly integrates entity recognition and disambi

Zhisheng Yang, Xiaofei Xu, Ke Deng, Li Li

As powerful tools in Natural Language Processing (NLP), Large Language Models (LLMs) have been leveraged for crafting recommendations to achieve precise alignment with user preferences and elevate the quality of the recommendations. The existing approaches implement both non-tuning and tuning strategies. Compared to following the tuning strategy, the approac

Emanuele Bernardi, Tommaso Lorenzi, Andrea Tosin

Building upon kinetic theory approaches for multi-agent systems and generalising them to scenarios where the total mass of the system is not conserved, we develop a modelling framework for phenotype-structured populations that makes it possible to bridge individual-level mechanisms with population-scale evolutionary dynamics. We start by formulating a stocha

Samuel Donachie, Ulysse Remond, Arthur Mathorel, Kyryl Kazymyrenko

Quantum computing holds great promise for solving classically intractable problems such as linear systems and partial differential equations (PDEs). While fully fault-tolerant quantum computers remain out of reach, current noisy intermediate-scale quantum (NISQ) devices enable the exploration of hybrid quantum-classical algorithms. Among these, Variational Q

Tomas Ortega, Hamid Jafarkhani

We propose the first parameter-free decentralized online learning algorithms with network regret guarantees, which achieve sublinear regret without requiring hyperparameter tuning. This family of algorithms connects multi-agent coin-betting and decentralized online learning via gossip steps. To enable our decentralized analysis, we introduce a novel "betting

V. M. Braun, K. G. Chetyrkin, A. N. Manashov

We use conformal symmetry to calculate the NNLO anomalous dimension matrix (three loops) for flavor-singlet axial-vector QCD operators for spin $N \le 8$ from a set of gauge-invariant two-point correlation functions. Combining this result with the recent calculation of the two-loop coefficient functions, we carry out the calculation of the $\gamma\gamma^\ast

Sian Brooke

In open-source software design, the inclusion of women is often highlighted simply to remind programmers that women exist. Yet, little attention is given to how greater gender diversity, specifically women's participation, could fundamentally alter development patterns. To understand the potential impact of gender inclusion, this study investigates React, a

Apurba Das, Fattoum Harrathi, Sami Mabrouk

A ternary Nambu-Poisson algebra (which we call a Nambu-Poisson algebra in the paper) is the underlying algebraic structure of Nambu-Poisson manifolds of order $3$ that appeared in the generalized Hamiltonian mechanics. First, we consider the 2nd cohomology group of a Nambu-Poisson algebra with coefficients in a given representation. Next, we discuss suitable

Manuel J. Fernandez, Alejandro Suarez, Anibal Ollero, Matteo Fumagalli

This paper presents the integration of a Variable Stiffness Link (VSL) for long-reach aerial manipulation, enabling adaptable mechanical coupling between an aerial multirotor platform and a dual-arm manipulator. Conventional long-reach manipulation systems rely on rigid or cable connections, which limit precision or transmit disturbances to the aerial vehicl

Jared K. Lepora, Haoran Li, Efi Psomopoulou, Nathan F. Lepora

This paper introduces an anthropomorphic robot hand built entirely using LEGO MINDSTORMS: the Educational SoftHand-A, a tendon-driven, highly-underactuated robot hand based on the Pisa/IIT SoftHand and related hands. To be suitable for an educational context, the design is constrained to use only standard LEGO pieces with tests using common equipment availab

Maki Komiya, Madoka Sato, Teng Ma, Hironori Kageyama

The electric field of biological membranes has long been treated as a one-dimensional quantity, defined solely by the component normal to the bilayer (E_VERT). Here, we present a bioelectronic platform that enables controlled generation of a horizontal electric field within the hydrophobic core of a lipid bilayer (E_HORZ). The device incorporates micrometer-

SiHao Fan, Chen Wu, WenJun Guo

We consider massless scalar field perturbations of regular black holes in quasi-topological gravity. After reviewing various regular black hole solutions, we compute the total absorption cross-section and the partial absorption cross-section for different choices of the parameters using the partial wave method. The results show that the larger the parameter

Amihay Hanany, Elias Van den Driessche

We compute the Higgs branch chiral ring of a simple class of 5d theories at strong coupling. A deformation by the instanton mass implies that the chiral ring at weak coupling is renormalized by a nilpotent operator, the gaugino bilinear. Consequently, F-term equations alone do not suffice to completely determine the moduli space of vacua and perturbative non

Louise A. M. Rosset, Volker L. Deringer

The silicon-hydrogen system is of key interest for solar-cell devices, including both crystalline and amorphous modifications. Elemental amorphous Si is now well understood, but the atomic-scale effects of hydrogenating the silicon matrix remain to be fully explored. Here, we present a machine-learned interatomic potential model based on the atomic cluster e

Max Welz

The association between a continuous and an ordinal variable is commonly modeled through the polyserial correlation model. However, this model, which is based on a partially-latent normality assumption, may be misspecified in practice, due to, for example (but not limited to), outliers or careless responses. The typically used maximum likelihood (ML) estimat

Joachim Rehberg, Elmar Schrohe

We study the regularity of solutions of elliptic second order boundary value problems on a bounded domain $\Omega$ in $\mathbb R^3$. The coefficients are not necessarily continuous and the boundary conditions may be mixed, i.e. Dirichlet on one part $D$ of the boundary and Neumann on the complementing part. The peculiarity is that $D$ is partly `buried' in $

Johannes Knörzer, Xiaoyu Liu, Benjamin F. Schiffer, Jordi Tura

Distributed quantum information processing seeks to overcome the scalability limitations of monolithic quantum devices by interconnecting multiple quantum processing nodes via classical and quantum communication. This approach extends the capabilities of individual devices, enabling access to larger problem instances and novel algorithmic techniques. Beyond

Varatharaja Nallathambi, Se-Ho Kim, Nikita Polin, Natalia F. Shkodich

Atom probe tomography (APT) provides the three-dimensional composition of materials at near-atomic length scales, achieving detection limits in the range of tens of atomic parts-per-million regardless of element type. APT requires the specimen to be shaped as a needle with a tip radius of ~100 nm. The development of site-specific lift-out procedures using fo

Mritunjay Tyagi, Simon Friederich

We present a compact, systematic formulation of the dynamics of the Husimi Q- and Glauber-Sudarshan P-phase space distribution functions expressed in terms of their \emph{complementary} Hamiltonian symbols: Anti-Wick for Q and Wick for P. The resulting evolution equations have a universal leading structure, the classical Liouvillian drift plus terms with hig

Lorenzo Costantini, Carla Sciarra, Luca Ridolfi, Francesco Laio

Centrality metrics aim to identify the most relevant nodes in a network. In literature, a broad set of metrics exists, either measuring local or global centrality characteristics. Nevertheless, when networks exhibit a high spectral gap, the usual global centrality measures typically do not add significant information with respect to the degree, i.e., the sim

Hongyu Zhou, Xiaoyu Zhang, Vasileios Tzoumas

We provide an algorithm for adaptive legged locomotion via online learning and model predictive control. The algorithm is composed of two interacting modules: model predictive control (MPC) and online learning of residual dynamics. The residual dynamics can represent modeling errors and external disturbances. We are motivated by the future of autonomy where

T. V. Pavlova, V. M. Shevlyuga

Dangling bonds (DBs) are common defects in silicon that affect its electronic performance by trapping carriers at the in-gap levels. For probing the electrical properties of individual DBs, a scanning tunneling microscope (STM) is an effective instrument. Here we study transitions between charge states of a single DB on chlorinated and brominated Si(100)-2$\

Ed Li, Junyu Ren, Xintian Pan, Cat Yan

The automation of scientific discovery represents a critical milestone in Artificial Intelligence (AI) research. However, existing agentic systems for science suffer from two fundamental limitations: rigid, pre-programmed workflows that cannot adapt to intermediate findings, and inadequate context management that hinders long-horizon research. We present \te

Shiang-Yu Huang, Jonas Zatsch, Tim Engling, Jeldrik Huster

Fiber-to-chip couplers play a crucial role in interfacing on-chip photonic circuits with other optical systems or off-chip devices. Downsizing the couplers via topology optimization addresses the demand for high-density integration and improves the scalability of photonic integrated systems. However, these optimized couplers have yet to reach the performance

Anubhab Ghosal

Generalising the Cameron--Erd\H{o}s conjecture to two dimensions, Elsholtz and Rackham conjectured that the number of sum-free subsets of $[n]^2$ is $2^{0.6n^2+O(n)}$. We prove their conjecture.

Jiahao Zhou, Chengliang Lin, Dingji Li, Mingkai Dong

Semantic top-K selection with cross-encoder rerankers underpins on-device AI services, such as retrieval-augmented generation, agent memory, and personalized recommendation. However, its latency and memory demands dominate end-to-end budgets on edge hardware. Revisiting the objective of top-K selection, we reveal that only relative rankings matter, not exact

A. Deltuva

The nonelastic breakup (NEB), one of channels in $(d,p)$ inclusive reactions, is studied using the Faddeev-type scattering theory. The NEB differential cross section is obtained in terms of the imaginary part of the neutron-nucleus optical potential sandwiched between the Alt-Grassberger-Sandhas three-body transition operators. The momentum-space calculation

Abdul-Nasah Soale, Adewale Lukman

An adaptive Cook's distance (ACD) for diagnosing influential observations in high-dimensional single-index models with multicollinearity and outlier contamination is proposed. ACD is a model-free technique built on sparse local linear gradients to temper leverage effects. In simulations spanning low- and high-dimensional design settings with strong correlati

Tiziano De Angelis, Jan Palczewski, Jacob Smith

This paper provides necessary and sufficient conditions for a pair of randomised stopping times to form a saddle point of a zero-sum Dynkin game with partial and/or asymmetric information across players. The framework is non-Markovian and covers essentially any information structure. Our methodology relies on the identification of suitable super and submarti

Shuchang Lyu, Qi Zhao, Zheng Zhou, Meng Li

Domain adaptation is a crucial and increasingly important task in remote sensing, aiming to transfer knowledge from a source domain a differently distributed target domain. It has broad applications across various real-world applications, including remote sensing element interpretation, ecological environment monitoring, and urban/rural planning. However, do

Clément Moureau, Thomas Stegen, Mevludin Glavic, Bertrand Cornélusse

This paper presents a method for predictive aggregation of the available flexibility at the residential unit level into a flexibility chart that represents the admissible active and reactive powers, along with the associated flexibility value. The method is also combined with centralized optimization to design a predictive privacy-preserving control scheme t

Tomáš Chobola, Julia A. Schnabel, Tingying Peng

Current self-supervised denoising techniques achieve impressive results, yet their real-world application is frequently constrained by substantial computational and memory demands, necessitating a compromise between inference speed and reconstruction quality. In this paper, we present an ultra-lightweight model that addresses this challenge, achieving both f

El Mahdi Chayti, Taha El Bakkali El Kadi, Omar Saadi, Martin Jaggi

We revisit random search for stochastic optimization, where only noisy function evaluations are available. We show that the method works under weaker smoothness assumptions than previously considered, and that stronger assumptions enable improved guarantees. In the finite-sum setting, we design a variance-reduced variant that leverages multiple samples to ac

Lin-Hong Sui, Dan Li, Jia-Ze Sun, Xi-Bin Li

We investigate an inflationary model wherein the Dirac field $\psi$ is directly coupled to a scalar inflaton $\phi$ via a Yukawa interaction $g\phi\bar\psi\psi$ and examine the resulting observational implications. Within the slow-roll approximation, we derive analytical solutions of the Dirac equations during inflation. The analytical result on the fermion